Data-constrained personalization of cognitive state detection with feature-based and foundation models

Despite growing interest in monitoring cognitive states, current studies inadequately address individual differences in physiological reactions. Whereas prior works require extensive data from each individual to personalize the model, the current article explores personalization approaches operating with minimal baseline data. We propose three novel methods to personalize the model with only baseline data available for personalization. Further, we systematically compare those to an existing baseline calibration method, a non-personalized model, and a model using all available data for personalization. We conduct experiments with four open datasets with a total of 170 participants, classifying the cognitive states with a prevalent feature-based approach and a recent large time-series foundation model, MOMENT. The experiments target stress and cognitive load detection in realistic classification tasks, which require models to adapt to a new person. The best classification scores after personalizing with minimal data were around 0.7−0.9 and 0.7 balanced accuracy in binary and three-class tasks, respectively. Two of the proposed personalization methods outperformed the non-personalized model in most cases with the feature-based approach, especially in classification tasks with more than two classes, although their performance remained lower than that of the model using all data for personalization. MOMENT showed little benefit from personalization and performed comparably to the feature-based approach even with a non-personalized model. The findings provide a critical overview of the generalizability and necessity of model personalization with little data, and valuable insights into the development of personalized cognition-aware applications.